Grazie, Antonio! jc On 20/03/23 13:36, Antonio Casilli wrote:
Grazie JC,
l'avevo visto e mi promettevo di commentarlo... La lista delle limitazioni di questo articolo è lunga quanto la somma di tutte le braccia rubate all'agricoltura dei suoi autori (tranne Daniel Rock, che qualche anno fa aveva posizioni molto meno oltranziste sulla questione, cf. https://www.nber.org/papers/w24001).
Questo documento è un ennesimo caso di LaTeX-driven advertising, nel senso che un paper con parvenza di scientificità viene usato per fare pubblicità o assecondare le operazioni di marketing di un'azienda. Un po' come il caso storico di Eytan Bakshy, Solomon Messing, Lada Adamic, “Exposure to ideologically diverse news and opinion on Facebook [archive]”, Science, 7, 2015, che scagionava l'algoritmo di NewsFeed di Facebook e dava la colpa agli utilizzatori per la creazione di “echo chambers”.
Da accogliere, come tutte le analisi task-based, con un sonoro "meh". Cheers, ---a
----- Original Message ----- From: "J.C. DE MARTIN" <juancarlos.demartin@polito.it> To: "Nexa" <nexa@server-nexa.polito.it> Sent: Monday, March 20, 2023 12:41:41 PM Subject: [nexa] "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models"
[Submitted on 17 Mar 2023] GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models [ https://arxiv.org/search/econ?searchtype=author&query=Eloundou%2C+T | Tyna Eloundou ] , [ https://arxiv.org/search/econ?searchtype=author&query=Manning%2C+S | Sam Manning ] , [ https://arxiv.org/search/econ?searchtype=author&query=Mishkin%2C+P | Pamela Mishkin ] , [ https://arxiv.org/search/econ?searchtype=author&query=Rock%2C+D | Daniel Rock ]
We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implications.
Subjects: General Economics (econ.GN) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: [ https://arxiv.org/abs/2303.10130 | arXiv:2303.10130 ] [econ.GN] (or [ https://arxiv.org/abs/2303.10130v1 | arXiv:2303.10130v1 ] [econ.GN] for this version) [ https://doi.org/10.48550/arXiv.2303.10130 | https://doi.org/10.48550/arXiv.2303.10130 ]
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